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Background: Parents play an important role in their children's oral health behaviors, provide oral health access, initiate prevention, and coping strategies for health care. Aim: This paper develops a short form (SF) to assist parents to evaluate their children's oral health status using Patient‐Reported Outcome Measurement Information System (PROMIS) framework that conceptualized health as physical, mental, and social components. Design: Surveys of parents were conducted at dental clinics in Los Angeles County, together with an on‐site clinical examination by dentists to determine clinical outcomes, Children's Oral Health Status Index (COHSI), and referral recommendations (RRs). Graded response models in item response theory were used to create the SF. A toolkit including SF, demographic information, and algorithms was developed to predict the COHSI and RRs. Results: The final SF questionnaire consists of eight items. The square root mean squared error for the prediction of COHSI is 7.6. The sensitivity and specificity of using SF to predict immediate treatment needs (binary RRs) are 85% and 31%. Conclusions: The parent SF is an additional component of the oral health evaluation toolkit that can be used for oral health screening, surveillance program, policy planning, and research of school‐aged children and adolescents from guardian perspectives. [ABSTRACT FROM AUTHOR]

• Based on AV collision reports, gaps in the safety performance of automated vehicles (AVs) are identified. • This study untangles relationships among pre-crash conditions, AV driving modes, and crash outcomes within a path-analytic framework. • AVs are manually disengaged when interacting with transit vehicles before a crash occurs. • AVs require thorough testing to adapt to critical infrastructures, such as intersections, ramps, and slip lanes. Vehicle automation, manifested in self-driving cars, promises to provide safe mobility by reducing human errors. While the testing of automated vehicles (AVs) has improved their performance in recent years, automation technologies face challenges such as uncertainty of safety impacts in mixed traffic with human-driven vehicles. This study aims to examine the gaps in AV safety performance and identify what will be required on a preferential basis for AVs to guarantee an acceptable level of safety performance, especially in mixed traffic, by conducting a thorough analysis of crashes involving levels 2–3 AVs. Based on 260 AV collision reports from California from 2019 to 2021, this study extracts crash-related variables from crash records in a standardized form, crash locations, and, notably, crash narratives reported by AV manufacturers. This study untangles the complex interrelationships among pre-crash conditions, AV driving modes, crash types, and crash outcomes by applying a path-analytic framework with the frequentist and Bayesian approaches. Results show that 51.9 percent of crashes were rear-ends. Particularly, AVs become more vulnerable to rear-end collisions in the automated driving mode than in the conventional mode, given a crash. Additionally, the automated driving mode would not significantly affect the chance of a sideswipe collision, injury, or AV damage levels. Another interesting finding is that manual disengagement is more likely to happen when an AV interacts with a transit vehicle right before a crash occurs while having a negative relationship with injury crashes. Moreover, to reduce injury crashes, AVs would need more thorough testing to adapt to the critical roadway and infrastructure features such as intersections, ramps, and slip lanes; and roadway infrastructure would require improvements to support transportation automation. The risk factors identified in this study can be considered in AV safety assessment scenarios and future operations of mixed traffic. This study demonstrates that AV crash narrative data can be leveraged to improve knowledge of AV safety in mixed traffic. [ABSTRACT FROM AUTHOR]

Background: Oral health is an important component of daily functioning and well-being. A comprehensive patient-reported oral health measure is needed to gauge the impact of oral health status on children and adolescents. This study aims to develop oral health item banks and associated short-form surveys for children and adolescents 2-17 year olds. Methods: Using children and adolescents, ages 2-17 years, selected from diverse dental sites in Greater Los Angeles Area, we propose to develop state-of-the-science methods to create oral health item banks to effectively measure oral health outcomes for children and adolescents. Methods include a literature review of existing measures, focus groups, cognitive interviews, drafting and field testing of survey items, and evaluation of the psychometric properties of the measures. Results: Based on the systematic literature search and focus groups, we identified core (physical health, mental health, and social function domains) and peripheral (e.g., need and access) oral health domains. We then drafted survey items and revised them based on 66 cognitive interviews (27 children/adolescents and 39 parents) with 39 families. The revised items will be administered in a field test of 500 children and adolescents ages 2-17, and their parents. Conclusions: The qualitative methods used in the initial phases of the project (focus group and cognitive interviews) are the initial steps in the development of oral health item banks and associated short-form surveys for children and adolescents. The oral health items can potentially be used to create effective computerized adaptive test and/or create ad hoc short forms targeting specific areas of oral health to survey large populations of children with much less cost compared with traditional clinical oral health examination. [ABSTRACT FROM AUTHOR]

Background: Although the association of the dopamine transporter (DAT1) gene and attention-deficit/hyperactivity disorder (ADHD) has been widely studied, far less is known about its potential interaction with environmental risk factors. Given that maltreatment is a replicated risk factor for ADHD, we explored the interaction between DAT1 and maltreatment with ADHD symptoms defined dimensionally and using latent class analysis (LCA). Method: We tested the association of the 40 base-pair variable number of tandem repeats polymorphism in DAT1, maltreatment, and their interaction in 2,488 boys and girls from the National Longitudinal Study of Adolescent Health. Results: In boys, ADHD symptoms were optimally defined by four classes (Combined, Hyperactive/Impulsive, Inattentive, and Normal), whereas in girls, ADHD symptoms were defined by three classes (Combined, Combined-Mild, Normal). A significant DAT1 × maltreatment interaction revealed that maltreated girls homozygous for the 10-repeat allele had more symptoms of ADHD, and were also 2.5 times more likely to be classified in the Combined ADHD group than in the Normal Group. Conclusions: The underlying structure of ADHD symptoms differed between boys and girls and DAT1 interacted with maltreatment to predict ADHD symptoms and ADHD status derived from LCA. Interactive exchanges between maltreatment and DAT1 for ADHD symptoms, and their implications for intervention, are discussed. [ABSTRACT FROM AUTHOR]

• AV collision reports in California were analyzed and then simulated in the CARLA environment. • A data pipeline was developed that using simulated data from all on-board AV sensors. • such as LiDAR, radar, cameras, IMU's, and GPS's, • Once processed, this data can provide crash investigators with new information about the state of the driver, the movement of vehicles, trajectories of other moving objects or people and the surrounding objects. As automated vehicles are deployed across the world, it has become critically important to understand how these vehicles interact with each other, as well as with other conventional vehicles on the road. One such method to achieve a deeper understanding of the safety implications for Automated Vehicles (AVs) is to analyze instances where AVs were involved in crashes. Unfortunately, this poses a steep challenge to crash-scene investigators. It is virtually impossible to fully understand the factors that contributed to an AV involved crash without taking into account the vehicle's perception and decision making. Furthermore, there is a tremendous amount of data that could provide insight into these crashes that is currently unused, as it also requires a deep understanding of the sensors and data management of the vehicle. To alleviate these problems, we propose a data pipeline that takes raw data from all on-board AV sensors such as LiDAR, radar, cameras, IMU's, and GPS's. We process this data into visual results that can be analyzed by crash scene investigators with no underlying knowledge of the vehicle's perception system. To demonstrate the utility of this pipeline, we first analyze the latest information on AV crashes that have occurred in California and then select two crash scenarios that are analyzed in-depth using high-fidelity synthetic data generated from the automated vehicle simulator CARLA. The data visualization procedure is demonstrated on the real-world Kitti dataset by using the YOLO object detector and a monocular depth estimator called AdaBins. Depth from LIDAR is used as ground truth to calibrate and assess the effect of noise and errors in depth estimation. The visualization and data analysis from these scenarios clearly demonstrate the vast improvement in crash investigations that can be obtained from utilizing state-of-the-art sensing and perception systems used on AVs. [ABSTRACT FROM AUTHOR]

After thyroid surgery, protocols based on postoperative parathyroid hormone (PTH) levels may prevent symptoms of hypocalcemia, while avoiding unnecessary prophylactic calcium and/or vitamin D supplementation. We examined the value of an initial management protocol based solely on a single PTH level measured one hour after completion or total thyroidectomy to prevent symptomatic hypocalcemia by conducting a retrospective review of 697 consecutive patients treated from July 2003 to April 2015. The proportion of patients who developed symptomatic hypocalcemia was similar between those treated before (n = 155) and after (n = 542) implementation of this 1-hour PTH protocol (16.8% vs 15.9%; P = 0.786). Those in the 1-hour PTH groups had lower overnight observation rates (97.4% vs 53.7%; P < 0.001) and length of stay (1.98 ± 2.61 vs 0.89 ± 1.87 days; P < 0.001), and required less calcium (3.9% vs 0.8%; P = 0.015) and vitamin D (2.6% vs 0%; P = 0.002) supplementation one year after surgery. Less than 1 per cent of patients discharged on the day of surgery in accordance with the 1-hour PTH guidelines returned to the emergency room for symptomatic hypocalcemia; none experienced significant morbidity. This protocol facilitates early discharge of low-risk patients and results in a similar or improved postoperative course compared with traditional overnight observation. [ABSTRACT FROM AUTHOR]

Purpose: To examine, in a retrospective study, whether the initial posttreatment mammogram offers any benefit to patients. Methods and Materials: Patients were selected who had radiation after breast-conservation therapy from 1995 through 2005 and had follow-up mammography at the University of California-Los Angeles (UCLA) within 1 year of completing radiotherapy. Results of the initial follow-up mammogram were analyzed to determine the yield of this initial mammogram. Results: Between 1995 and 2005, 408 patients treated with breast-conserving therapy and radiation had follow-up mammograms at UCLA within 1 year of completion of radiation. Median age at radiation completion was 56.9 years. Median interval between radiation and the initial mammogram was 3.1 months. Ten patients were found to have suspicious findings on the initial postradiation mammogram, prompting biopsy, but only 2 were found to have recurrent cancer. None of those lesions were palpable. In both cases the recurrences were ductal carcinoma in situ. Thus, the yield of the initial postoperative mammogram as compared with physical examination findings is estimated at 0.49 recurrences detected per 100 mammograms performed (95% confidence interval 0.059–1.759). Conclusions: The yield of the initial postradiation mammography at UCLA seems to be low, and only noninvasive carcinomas were found. Our data support the rationale to avoid the initial short-interval postradiation mammography and evaluate patients at 12 months. [Copyright &y& Elsevier]